Agentic QA Engineer

Agentic QA Engineer – Generative AI & Agentic Systems (Agent, Multi‑Agent Testing)

Location: Dallas, TX - Onsite

Duration: Long term

Summary

We are seeking a hands-on AI Engineer to design and execute end-to-end testing strategies for agentic AI solutions, including multi-agent systems in production-grade environments. This role partners with the Agentic Operations Team to ensure resiliency, reliability, accuracy, latency, orchestration correctness, and scale.

Key Responsibilities

Agentic & Multi‑Agent Testing
Reliability, Resiliency, and Latency
Accuracy & Macro-Level Validations
Scale & Orchestration
Dev → Prod Readiness
Define and own the QA strategy for agentic/multi-agent AI systems across dev, staging, and prod.
Mentor a team of QA engineers; establish testing standards, coding guidelines for test harnesses, and review practices.
Partner with Agentic Operations, Data Science, MLOps, and Platform teams to embed QA in the SDLC and incident response.
Design tests for agent orchestration, tool calling, planner-executor loops, and
Establish latency SLOs and measure end-to-end response times across orchestration layers (LLM calls, tool invocations, queues).
Ensure reliability through soak tests, canary verifications, and automated rollbacks.
Define ground-truth and reference pipelines for task accuracy (exact match, semantic similarity, factuality checks).
Build macro validation frameworks that validate task outcomes across multi-step agent workflows (e.g., complex data pipelines, content generation + verification agent loops).
Instrument guardrail validations (toxicity, PII, hallucination, policy compliance).

Required Qualifications
7+ years in Software QA/Testing, with 2+ years in AI/ML or LLM-based systems; hands-on experience testing agentic/multi-agent architectures.
Strong programming skills in Python experience building test harnesses, simulators, and fixtures.
Experience with LLM evaluation (exact/soft match, BLEU/ROUGE, BERTScore, semantic similarity via embeddings), guardrails, and prompt testing.
Expertise in distributed systems testing latency profiling, resiliency patterns (circuit breakers, retries), chaos engineering, and message queues.
Familiarity with orchestration frameworks (LangChain, LangGraph, LlamaIndex, DSPy, OpenAI Assistants/Actions, Azure OpenAI orchestration, or similar).
Proficiency with CI/CD (GitHub Actions/Azure DevOps), observability (OpenTelemetry, PrometheGrafana, Datadog), and feature flags/canaries.

Preferred Qualifications
Experience with multi-agent simulators, agent graph testing, and tooling latency emulation.
Knowledge of MLOps (model versioning, datasets, evaluation pipelines) and A/B experimentation for LLMs.
Background in cloud (AWS), serverless, containerization, and event-driven architectures.